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17th Asian Conference on Computer Vision, ACCV 2024

作者： Viriyasaranon, Thanaporn Ma, Serie Choi, Jang-Hwan Department of Computational Medicine Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Korea Republic of Division of Mechanical and Biomedical Engineering Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Korea Republic of Department of Artificial Intelligence Ewha Womans University Seoul Korea Republic of

ISBN: (纸本)9789819609000

The precise detection of cephalometric landmarks on two-dimensional (2D) radiographs or three-dimensional (3D) computed tomography (CT) images is a fundamental step in various medical fields, especially in research on orthodontics and maxillofacial surgery. Deep learning-based detectors have demonstrated remarkable accuracy in 2D cephalometric analysis, whereas conventional single-view approaches are limited by their reliance on information from a single perspective. This study proposes GeoRefineNet, a novel multistage framework that leverages information from multiple CT scans acquired at various angles. By incorporating geometric knowledge through a 3D heatmap reconstruction process, GeoRefineNet improves robustness, accuracy, and adaptability to various cephalometric configurations. The proposed framework predicts 3D landmark positions on CT images, effectively addressing challenges associated with high-dimensional input data and limited training examples. GeoRefineNet surpasses the existing state-of-the-art models in the 2D and 3D domains, as demonstrated by its superior performance on numerical and clinical datasets. These findings indicate that GeoRefineNet offers a promising avenue for improving the accuracy and reliability of cephalometric landmark detection fostering further advances in clinical diagnosis and treatment planning. Our code is available at https://***/Thanaporn09/***. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computerized tomography

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Energy-Free Sensing and Context Recognition Using Photovoltaic Cells

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IEEE Sensors Journal 2025年第9期25卷 15598-15611页

作者： Shintani, Kaede Rizk, Hamada Yamaguchi, Hirozumi Osaka University Graduate School of Information Science and Technology Department of Information Networking Suita565-0871 Japan Tanta University Tanta31733 Egypt RIKEN Center for Computational Science Kobe650-0047 Japan RIKEN Center for Computational Science Department of Computer and Control Engineering Kobe650-0047 Japan

The field of energy-free sensing and context recognition has recently gained significant attention as it allows operating systems without external power sources. Photovoltaic cells can convert light energy into electrical energy to power sensing devices, but their power may not be sufficient to ensure energy-free sensing due to the varying power needs of sensors and high computational demands. In this article, we propose the use of photovoltaic cells as a standalone sensor for the recognition of different contexts, including user identification, step counting, and location tracking. The system utilizes the photocurrent readings generated by the photovoltaic cells to capture the unique mobility patterns of different users. By analyzing these patterns, the system can accurately identify the user, count the number of steps taken, and track the user’s location. We propose a computationally efficient dynamic time warping (DTW) to match the variable length sequences of photocurrent readings to a database of known patterns and identify the closest subject and location matches. The system was rigorously evaluated in a realistic environment, and the results indicate that it can accurately estimate step count, identify subjects, and localize them with an accuracy of 88%, 90%, and 43 cm, respectively. This is achieved while the proposed system is nonintrusive and can operate without external power sources, making it a promising technology for energy-free sensing and context recognition. © 2001-2012 IEEE.

关键词： Photovoltaics

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Rheological and stability analysis of cement pastes incorporating silica-based wastes

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Particuology 2024年第6期89卷 144-152页

作者： Julia da Rosa Martins Janaíde Cavalcante Rocha Dachamir Hotza Luciano Senff Graduate Program in Materials Science and Engineering(PPGMAT) Federal University of Santa Catarina(UFSC)Florianópolis88040-900SCBrazil Graduate Program in Civil Engineering(PPGEC) Federal University of Santa Catarina(UFSC)Florianópolis88040-900SCBrazil Graduate Program in Chemical Engineering(POSENQ) Federal University of Santa Catarina(UFSC)Florianópolis88040-900SCBrazil Department of Mobility Engineering Federal University of Santa Catarina(UFSC)Joinville89219-600SCBrazil

This study investigates the influence of waste characteristics,especially zeta potential,on the properties of cement pastes and *** focus is to evaluate the impact of the zeta potential of cement particles and waste materials on the sedimentation speed,rheology,and hardening time of stabilized cement *** Cement II F 40,retarder additive,silica,and fly ash were used in the *** pastes were prepared,and during the stabilization period,their rheological properties and pH were *** zeta potential and sedimentation speed of the cement and waste particles were measured at the pH that the pastes presented during the entire stabilization *** the stabilization period,the pastes were subjected to the hardening time *** zeta potential analyses revealed diverse values for the different powder types,with the cement particles exhibiting a zeta potential of−3.0 mV,the silica particles exhibiting−10.5 mV,and the fly ash particles exhibiting−20.3 *** influence of the high zeta potential modulus was observed on the sedimentation speed,with the solution containing fly ash exhibiting a speed of 40.01μm/s,whereas the solution containing only cement exhibited a speed of 99.38μm/*** the pastes,the results indicate that the presence of fly ash particles with a significantly negative zeta potential led to a 16%reduction in hardening time compared to particles with a lower modulus of zeta *** tests showed that the inclusion of fly ash particles prevented the formation of *** the zeta potential influenced agglomerate formation and hardening time,it was found to have no effect on yield stress or viscosity.

关键词： Wastes Zeta potential Setting time Rheometry

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Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete

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Frontiers of Structural and Civil engineering 2023年第2期17卷 284-305页

作者： Hai-Van Thi MAI May Huu NGUYEN Son Hoang TRINH Hai-Bang LY Civil Engineering Department University of Transport TechnologyHanoi 100000Vietnam Civil and Environmental Engineering Program Graduate School of Advanced Science and EngineeringHiroshima UniversityHiroshima 739-8527Japan

Fiber-reinforced self-compacting concrete(FRSCC)is a typical construction material,and its compressive strength(CS)is a critical mechanical property that must be adequately *** the machine learning(ML)approach to estimating the CS of FRSCC,the current research gaps include the limitations of samples in databases,the applicability constraints of models owing to limited mixture components,and the possibility of applying recently proposed *** study developed different ML models for predicting the CS of FRSCC to address these *** neural network,random forest,and categorical gradient boosting(CatBoost)models were optimized to derive the best predictive model with the aid of a 10-fold cross-validation technique.A database of 381 samples was created,representing the most significant FRSCC dataset compared with previous studies,and it was used for model *** findings indicated that CatBoost outperformed the other two models with excellent predictive abilities(root mean square error of 2.639 MPa,mean absolute error of 1.669 MPa,and coefficient of determination of 0.986 for the test dataset).Finally,a sensitivity analysis using a partial dependence plot was conducted to obtain a thorough understanding of the effect of each input variable on the predicted CS of *** results showed that the cement content,testing age,and superplasticizer content are the most critical factors affecting the CS.

关键词： compressive strength self-compacting concrete artificial neural network decision tree CatBoost

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Comments on "relaxed Conditions for the Input-to-State Stability of Switched Nonlinear Time-Varying Systems"

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IEEE Transactions on Automatic Control 2025年第6期70卷 4233-4236页

作者： Şahan, Gökhan Trenn, Stephan Izmir Institute of Technology Department of Mathematics and Computational Science and Engineering Program Urla Izmir Turkey University of Groningen Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence Groningen9722BA Netherlands

This study addresses the deficiencies in the assumptions of the results in Chen and Yang, 2017 [1] due to the lack of uniformity. We first show the missing hypothesis by presenting a counterexample. Then we prove why they are wrong in that form and show the errors in the proof of the main result of [1]. Next, we compare the assumptions and related results of [1] with similar works in the literature. Lastly, we give suggestions to complement the shortcomings of the hypotheses and thus correct them. © 1963-2012 IEEE.

关键词： Nonlinear simulations

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QoE Comparison between Multi-User OFDMA and Reliable Groupcast for Wireless LAN Audiovisual Transmission

QoE Comparison between Multi-User OFDMA and Reliable Groupca...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Deguchi, Daiki Nunome, Toshiro Graduate School of Engineering Nagoya Institute of Technology Computer Science Program Department of Engineering Nagoya466-8555 Japan

ISBN: (纸本)9798350364866

This paper compares video and audio QoE of OFDMA multi-user transmission and reliable groupcast over wireless LAN. We assume video and audio transmission to several terminals simultaneously. As a reliable groupcast method, we employ Unsolicited Retry, a technique of IEEE 802.11aa GroupCast with Retries. We utilize IEEE 802.11ax for OFDMA transmission. We evaluate application-level QoS by computer simulation. We then assess QoE through a subjective experiment with video and audio streams generated by the output timing obtained from the simulation. We notice that each method has a situation to be applied appropriately. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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MPEG-DASH Multi-View Video and Audio Transmission QoE Over HTTP/2 Stream

MPEG-DASH Multi-View Video and Audio Transmission QoE Over H...

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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Wakayama, Yamato Nunome, Toshiro Graduate School of Engineering Nagoya Institute of Technology Computer Science Program Department of Engineering Nagoya466-8555 Japan

ISBN: (纸本)9798350353983

In this paper, we consider multi-view video and audio streaming using MPEG-DASH, which enables to transmit video tailored to the network conditions over HTTP communication. This paper uses HTTP/2 instead of HTTP/1.1, which the authors previously used. HTTP/2 manages a series of request-response exchanges called a stream, which is assigned a unique stream ID. We perform a subjective experiment under various network conditions to evaluate application-level QoS and QoE. From the evaluation results, we investigate the effect of the HTTP/2 stream on the video and audio quality of the users in the multi-view video transmission scenario. © 2024 IEEE.

关键词： Audio streaming

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A dive into spectral inference networks: improved algorithms for self-supervised learning of continuous spectral representations

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Applied Mathematics and Mechanics(English Edition) 2023年第7期44卷 1199-1224页

作者： J.WU S.F.WANG P.PERDIKARIS Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania PhiladelphiaPA 19104U.S.A. Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania PhiladelphiaPA 19104U.S.A.

We propose a self-supervising learning framework for finding the dominant eigenfunction-eigenvalue pairs of linear and self-adjoint *** represent target eigenfunctions with coordinate-based neural networks and employ the Fourier positional encodings to enable the approximation of high-frequency *** formulate a self-supervised training objective for spectral learning and propose a novel regularization mechanism to ensure that the network finds the exact eigenfunctions instead of a space spanned by the ***,we investigate the effect of weight normalization as a mechanism to alleviate the risk of recovering linear dependent modes,allowing us to accurately recover a large number of *** effectiveness of our methods is demonstrated across a collection of representative benchmarks including both local and non-local diffusion operators,as well as high-dimensional time-series data from a video *** results indicate that the present algorithm can outperform competing approaches in terms of both approximation accuracy and computational cost.

关键词： spectral learning partial differential equation(PDE) neural network slow features analysis

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Sorption-enhanced steam gasification of fine coal waste for fuel producing

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Journal of King Saud University - engineering sciences 2024年第2期36卷 81-88页

作者： Aprianti, Nabila Faizal, Muhammad Said, Muhammad Nasir, Subriyer Doctoral Program of Environmental Science Graduate School Universitas Sriwijaya South Sumatra Palembang30139 Indonesia Chemical Engineering Department Faculty of Engineering Universitas Sriwijaya South Sumatra Ogan Ilir30662 Indonesia

Improving the quality of syngas from fine coal waste using the sorption-enhanced gasification process is a novel technology in the production of H2. The effect of CaO on CO2 absorption and H2 increase in the steam fine coal gasification process was determined in a fixed bed gasifier. The steam gasification process took place at 650 °C using bentonite and CaO as catalysts and absorbents. Steam increased the H2 concentration in the syngas to 58 vol%. In-situ CO2 absorption is more effective with the addition of CaO. The maximum percentage of CO2 was absorbed when the Ca/C ratio 2 was 78.33 %. The H2 content in the syngas after the CO2 was absorbed increased rapidly to 75.80 vol% at a Ca-to-carbon-mole ratio (Ca/C) of 1.5 and a steam-to-feedstock ratio (S/F) of 1.5. CaO did not produce significant results for low heating value (LHV) or cold gas efficiency (CGE), with results of 12 MJ/Nm3 and 44.53 %. The dominant water gas shift reaction due to the influence of steam and CaO increased H2/CO up to 9.11, which made the syngas from this work suitable for Fischer–Tropsch synthesis. © 2022 The Authors

关键词： Carbon dioxide

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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